ReviewFrontiers in neurology2023
A wearable device for at-home obstructive sleep apnea assessment: State-of-the-art and research challenges.
Review in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Non-Contact Detection of Apnea-like Breathing Cessations Using Laser Speckle Pattern Analysis.Sensors (Basel, Switzerland) · 2026Article
- ReSTech project on Xiaomi wearable devices for monitoring and detecting obstructive sleep apnoea: observational study protocol.BMJ open · 2025Article
- Wearable Artificial Intelligence for Sleep Disorders: Scoping Review.Journal of medical Internet research · 2025Article
- Transforming Sleep Monitoring: Review of Wearable and Remote Devices Advancing Home Polysomnography and Their Role in Predicting Neurological Disorders.Biosensors · 2025Review
- Diagnostic Accuracy of the Alice NightOne Single-Belt Monitor for Obstructive Sleep Apnea and Reliability of Wireless Data Transfer.Nature and science of sleep · 2025Article
- A clinical-translational review of sleep problems in neurodevelopmental disabilities.Journal of neurodevelopmental disorders · 2024Review
- Digital health delivery in respiratory medicine: adjunct, replacement or cause for division?European respiratory review : an official journal of the European Respiratory Society · 2024Review
- Piezoelectric rubber sheet sensor: a promising tool for home sleep apnea testing.Sleep & breathing = Schlaf & Atmung · 2024Article
- Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management.Cell metabolism · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last 3 years, almost all medical resources have been reserved for the screening and treatment of patients with coronavirus disease (COVID-19). Due to a shortage of medical staff and equipment, diagnosing sleep disorders, such as obstructive sleep apnea (OSA), has become more difficult than ever. In addition to being diagnosed using polysomnography at a hospital, people seem to pay more attention to alternative at-home OSA detection solutions. This study aims to review state-of-the-art assessment techniques for out-of-center detection of the main characteristics of OSA, such as sleep, cardiovascular function, oxygen balance and consumption, sleep position, breathing effort, respiratory function, and audio, as well as recent progress in the implementation of data acquisition and processing and machine learning techniques that support early detection of severe OSA levels.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.